Right now the neat future it have is the ability of running custom filters of varied window size of images, and use custom formulas to blend several images
I don't have a tutorial at hand on how to use it, but I have a YouTube video where I show some of its features
Classical machine vision and pattern recognition is absolutely AI. Or at least it was AI before it became too mature to be called that. As they say, any AI problem that gets solved stops being AI and becomes just normal algorithmics.
I had recently learned about using image pyramids[1] in conjunction with template matching algorithms like SAD to do simple and efficient object recognition, it was quite fun.
It may come as a surprise to some that a lot of industrial computer vision is done in grayscale. In a lot of industrial CV tasks, the only things that matter are cost, speed, and dynamic range. Every approach we have to making color images compromises on one of those three characteristics.
I think this kind of thing might have real, practical use cases in industry if it's fast enough.
i don't have any background in computer vision but enjoyed how the introductory chapter gets right into it illustrating how to build a limited but working simple vision system
About 15, 20 years ago I was still in uni and we had a computer vision lab, the main guy there had been working on that subject for years and dealt with businesses where his stuff was used for quality control.
Without fail, step one of computer vision was to bring the image down to grayscale and / or filter for specific colours so you ended up with a 1 bit representation.
My "algorithm" for a robot that was to follow a line drawn on the floor boiled down to "filter out the colour green, then look at the bottom rows of the image and find the black pixels. If they're to the left, adjust to the left, if to the right adjust to the right". Roughly. I'm sure it could be done a lot more cleverly but I was pretty proud of it AND the whole tool suite was custom made, from editing environment to programming language. Expensive cameras and robot, too.
The blob-finding algorithm makes me think of the "advent of code" problems - I wouldn't have thought to do a two-pass approach, but now that I see it set out in front of me it's obviously a great idea. Seems like this technique could quite easily be generalised to work with a range of problems.
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[ 4.2 ms ] story [ 56.1 ms ] threadRight now the neat future it have is the ability of running custom filters of varied window size of images, and use custom formulas to blend several images
I don't have a tutorial at hand on how to use it, but I have a YouTube video where I show some of its features
https://youtube.com/playlist?list=PL3pnEx5_eGm9rVr1_u1Hm_LK6...
I had recently learned about using image pyramids[1] in conjunction with template matching algorithms like SAD to do simple and efficient object recognition, it was quite fun.
1: https://en.wikipedia.org/wiki/Pyramid_%28image_processing%29
A truly clever image processing method.
I think this kind of thing might have real, practical use cases in industry if it's fast enough.
prior hn thread: https://news.ycombinator.com/item?id=44281506
i don't have any background in computer vision but enjoyed how the introductory chapter gets right into it illustrating how to build a limited but working simple vision system
https://github.com/zserge?tab=repositories&q=&type=&language...
Without fail, step one of computer vision was to bring the image down to grayscale and / or filter for specific colours so you ended up with a 1 bit representation.
My "algorithm" for a robot that was to follow a line drawn on the floor boiled down to "filter out the colour green, then look at the bottom rows of the image and find the black pixels. If they're to the left, adjust to the left, if to the right adjust to the right". Roughly. I'm sure it could be done a lot more cleverly but I was pretty proud of it AND the whole tool suite was custom made, from editing environment to programming language. Expensive cameras and robot, too.